Application-Based Cooperative Content Caching via Tensor Domain Adaptation Networks
Yanlong Wang, Youjia Chen, Pingping Chen, Jinsong Hu, Haifeng Zheng · 2022
The proliferation of smart devices has accelerated the development of mobile applications (Apps). Traffic generated by Apps accounts for the vast majority of cellular traffic, resulting in significant transmission delay. Caching popular App content can effectively reduce duplicate data transmission. In this paper, we design an App-based cooperative content caching scheme through Apps' traffic prediction. In detail, a tensor domain adaptation network model for Apps' traffic prediction is proposed, where cross-domain spatio-temporal features are transferred from the source domain to the target domain by learning a shared tensor subspace. Furthermore, a cooperative content caching strategy is proposed to minimize the service delay. The cache decision problem is solved by GA-PSO which jointly uses a genetic algorithm and a particle swarm optimization. Experiment results show the proposed prediction model outperforms existing models in terms of prediction accuracy, and the performance impact of different caching modes and storage sizes is also Investigated.